Senior Machine Learning Engineer - Healthcare AI

About the Role

This is a senior individual contributor role on a growing AI and Data Science team, focused on building and owning production-grade machine learning solutions in the healthcare space. You'll operate across the full ML lifecycle — from data preparation through to deployment and ongoing monitoring — with direct impact on clinical and operational outcomes.

What You'll Do

• Design, develop, deploy, and maintain enterprise-scale machine learning solutions end-to-end.
• Build ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, monitoring, and retraining.
• Develop and maintain MLOps infrastructure including CI/CD pipelines, model registry, feature stores, automated deployment, and rollback strategies.
• Monitor production models for drift, accuracy degradation, and overall system health.
• Develop REST APIs and integrate ML services into enterprise cloud applications.
• Optimize models for latency, scalability, reliability, and cost.
• Provide technical leadership on AI/ML initiatives across engineering, product, and clinical stakeholders.
• Ensure all solutions comply with HIPAA, PHI/PII handling, and enterprise security standards.

What We're Looking For

• 8+ years of professional software engineering and machine learning experience.
• Strong, mandatory background in the healthcare industry, including working with sensitive health data under HIPAA and related compliance frameworks.
• Hands-on expertise across the full ML lifecycle: preprocessing, feature engineering, model development, calibration, deployment, and maintenance.
• Proficiency in Python and SQL; strong debugging and performance-tuning skills.
• Experience with distributed computing (Apache Spark) and Databricks in production environments.
• Familiarity with MLflow, feature stores, model registries, and CI/CD tooling for ML.
• Cloud platform experience across Azure, AWS, and/or GCP; comfort with Docker and Git; Kubernetes a plus.
• Experience with LLMs in production, RAG/prompt engineering, or GenAI tooling (e.g. Azure ML, SageMaker, Vertex AI) is a plus.
• Strong communication skills and an ownership mindset in cross-functional environments.
• Must be authorized to work in the US; visa sponsorship is not available.